DTI in Diagnosis and Follow-Up of Brain Tumors

  • Frank De Belder
  • Sophie Van Cauter
  • Luc van den Hauwe
  • Wim Van Hecke
  • Louise Emsell
  • Maya De Belder
  • Matthias Spaepen
  • Stefan Sunaert
  • Paul M. Parizel

Abstract

DTI is distinguished from DWI by its sensitivity to anisotropy or directionally dependent diffusion. Therefore, DTI allows the study of microstructural characteristics of the measured tissue. This fairly new imaging technique is mainly used to study macroscopic axonal organization in nervous system tissues. It has been shown that anisotropy in the white matter of the brain is, in part, determined by axonal density as well as myelin status. Changes in diffusion in pathological processes, such as tumors, can have several causes, such as the loss of tissue organization or changes in the extracellular space. Furthermore, brain tumors induce the disruption or displacement of white matter structures, the widening of fiber bundles due to tumor infiltration or edema. The principal application of DTI in the neuroradiology of brain tumors is DTI tractography for intraoperative guidance in tumor resection, as covered in the previous chapter. This chapter reviews the potential role of DTI in the (differential) diagnosis and follow-up of brain tumors, and special challenges associated with this application in clinical practice.

Keywords

Characterizing brain tumors Tumor grade Surgical planning Monitoring treatment effects 

References

  1. 1.
    Pierallini A, Caramia F, Falcone C, Tinelli E, Paonessa A, Ciddio AB, et al. Pituitary macroadenomas: preoperative evaluation of consistency with diffusion-weighted MR imaging--initial experience. Radiology. 2006;239(1):223–31.CrossRefPubMedGoogle Scholar
  2. 2.
    Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol. 2003;24(5):937–41.PubMedGoogle Scholar
  3. 3.
    Zhou X, et al. A DTI study of glioma infiltration using fractional anisotropy and fiber coherence index. Proc Intl Soc Mag Reson Med. 2007;15:344.Google Scholar
  4. 4.
    Ebisu T, et al. Discrimination of brain abscess from necrotic or cystic tumors by diffusion-weighted echo planar imaging. Magn Reson Imaging. 1996;14(9):1113–6.CrossRefPubMedGoogle Scholar
  5. 5.
    Hakyemez B. Glioblastoma multiforme with atypical diffusion-weighted MR findings. Br J Radiol. 2005;78(935):989–92.CrossRefPubMedGoogle Scholar
  6. 6.
    Reiche W, Schuchardt V, Hagen T, II’yasov KA, Billmann P, Weber J. Differential diagnosis of intracranial ring enhancing cystic mass lesions-role of diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI). Clin Neurol Neurosurg. 2010;112(3):218–25.CrossRefPubMedGoogle Scholar
  7. 7.
    Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol. 2004;25(3):356–69.PubMedGoogle Scholar
  8. 8.
    Toh C, et al. Differentiation of brain abscesses from necrotic glioblastomas and cystic metastatic brain tumors with diffusion tensor imaging. AJNR Am J Neuroradiol. 2011;32(9):1646–51.CrossRefPubMedGoogle Scholar
  9. 9.
    Brandsma D, van den Bent MJ. Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol. 2009;22(6):633–8.CrossRefPubMedGoogle Scholar
  10. 10.
    Wang S, Kim S, Chawla S, Wolf RL, Zhang W-G, O’Rourke DM, et al. Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging. Neuroimage. 2009;44(3):653–60.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Sinha S, Bastin ME, Whittle IR, Wardlaw JM. Diffusion tensor MR imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol. 2002;23(4):520–7.PubMedGoogle Scholar
  12. 12.
    Gupta RK, Hasan KM, Mishra AM, Jha D, Husain M, Prasad KN, et al. High fractional anisotropy in brain abscesses versus other cystic intracranial lesions. AJNR Am J Neuroradiol. 2005;26(5):1107–14.PubMedGoogle Scholar
  13. 13.
    Kumar M, Gupta RK, Nath K, Rathore RKS, Bayu G, Trivedi R, et al. Can we differentiate true white matter fibers from pseudofibers inside a brain abscess cavity using geometrical diffusion tensor imaging metrics? NMR Biomed. 2008;21(6):581–8.CrossRefPubMedGoogle Scholar
  14. 14.
    Bonneville F, Savatovsky J, Chiras J. Imaging of cerebellopontine angle lesions: an update. Part 2: intra-axial lesions, skull base lesions that may invade the CPA region, and non-enhancing extra-axial lesions. Eur Radiol. 2007;17(11):2908–20.CrossRefPubMedGoogle Scholar
  15. 15.
    Koot RW, Jagtap AP, Akkerman EM, Den Heeten GJ, Majoie CBLM. Epidermoid of the lateral ventricle: evaluation with diffusion-weighted and diffusion tensor imaging. Clin Neurol Neurosurg. 2003;105(4):270–3.CrossRefPubMedGoogle Scholar
  16. 16.
    Thurnher MM. Diffusion-weighted MR, imaging (DWI) in two intradural spinal epidermoid cysts. Neuroradiology. 2012;54(11):1235–6.CrossRefPubMedGoogle Scholar
  17. 17.
    Lian K, Schwartz ML, Bilbao J, Perry J, Aviv RI, Symons SP. Rare frontal lobe intraparenchymal epidermoid cyst with atypical imaging. J Clin Neurosci. 2012;19(8):1185–7.CrossRefPubMedGoogle Scholar
  18. 18.
    Osborn AG, Preece MT. Intracranial cysts: radiologic-pathologic correlation and imaging approach 1. Radiology. 2006;239(3):650–64.CrossRefPubMedGoogle Scholar
  19. 19.
    Chen S, Ikawa F, Kurisu K, Arita K, Takaba J, Kanou Y. Quantitative MR evaluation of intracranial epidermoid tumors by fast fluid-attenuated inversion recovery imaging and echo-planar diffusion-weighted imaging. AJNR Am J Neuroradiol. 2001;22(6):1089–96.PubMedGoogle Scholar
  20. 20.
    Annet L, Duprez T, Grandin C, Dooms G, Collard A, Cosnard G. Apparent diffusion coefficient measurements within intracranial epidermoid cysts in six patients. Neuroradiology. 2014;44(4):326–8.CrossRefGoogle Scholar
  21. 21.
    Hakyemez B, Aksoy U, Yildiz H, Ergin N. Intracranial epidermoid cysts: diffusion-weighted, FLAIR and conventional MR findings. Eur J Radiol. 2005;54(2):214–20.CrossRefPubMedGoogle Scholar
  22. 22.
    Santhosh K, Thomas B, Radhakrishnan VV, Saini J, Kesavadas C, Gupta AK, et al. Diffusion tensor and tensor metrics imaging in intracranial epidermoid cysts. J Magn Reson Imaging. 2009;29(4):967–70.CrossRefPubMedGoogle Scholar
  23. 23.
    Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M. Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. J Magn Reson Imaging. 2006;24(4):817–24.CrossRefPubMedGoogle Scholar
  24. 24.
    Lucchinetti CF, Gavrilova RH, Metz I, Parisi JE, Scheithauer BW, Weigand S, et al. Clinical and radiographic spectrum of pathologically confirmed tumefactive multiple sclerosis. Brain. 2008;131(7):1759–75.PubMedCentralCrossRefPubMedGoogle Scholar
  25. 25.
    Toh C, et al. Differentiation of tumefactive demyelinating lesions from high-grade gliomas with the use of diffusion tensor imaging. AJNR Am J Neuroradiol. 2012;33(5):846–51.CrossRefPubMedGoogle Scholar
  26. 26.
    Masu K, Beppu T, Fujiwara S, Kizawa H, Kashimura H, Kurose A, et al. Proton magnetic resonance spectroscopy and diffusion-weighted imaging of tumefactive demyelinating plaque. Neurol Med Chir. 2009;49(9):430–3.CrossRefGoogle Scholar
  27. 27.
    Barnholtz-Sloan JS. Incidence proportions of brain metastases in patients diagnosed (1973 to 2001) in the metropolitan detroit cancer surveillance system. J Clin Oncol. 2004;22(14):2865–72.CrossRefPubMedGoogle Scholar
  28. 28.
    Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology. 2002;222(3):715–21.CrossRefPubMedGoogle Scholar
  29. 29.
    Tang YM, Ngai S, Stuckey S. The solitary enhancing cerebral lesion: can FLAIR aid the differentiation between glioma and metastasis? AJNR Am J Neuroradiol. 2006;27(3):609–11.PubMedGoogle Scholar
  30. 30.
    Maurer M, Synowitz M, Badakshi H, Lohkamp L, Wüstefeld J, Schäfer ML, et al. Glioblastoma multiforme versus solitary supratentorial brain metastasis: differentiation based on morphology and magnetic resonance signal characteristics. Fortschr Röntgenstr. 2013;185(03):235–40.Google Scholar
  31. 31.
    Chiang IC, Kuo Y-T, Lu C-Y, Yeung K-W, Lin W-C, Sheu F-O, et al. Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology. 2004;46(8):619.CrossRefPubMedGoogle Scholar
  32. 32.
    Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22(6):1081–8.PubMedGoogle Scholar
  33. 33.
    Stadnik TW, Chaskis C, Michotte A, Shabana WM, van Rompaey K, Luypaert R, et al. Diffusion-weighted MR imaging of intracerebral masses: comparison with conventional MR imaging and histologic findings. AJNR Am J Neuroradiol. 2001;22(5):969–76.PubMedGoogle Scholar
  34. 34.
    Bulakbasi N, Kocaoglu M, Ors F, Tayfun C, Uçöz T. Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR Am J Neuroradiol. 2003;24(2):225–33.PubMedGoogle Scholar
  35. 35.
    Yamasaki F, Kurisu K, Satoh K, Arita K, Sugiyama K, Ohtaki M, et al. Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology. 2005;235(3):985–91.CrossRefPubMedGoogle Scholar
  36. 36.
    Calli C, Kitis O, Yunten N, Yurtseven T, Islekel S, Akalin T. Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol. 2006;58(3):394–403.CrossRefPubMedGoogle Scholar
  37. 37.
    Rollin N, Guyotat J, Streichenberger N, Honnorat J, Tran Minh V-A, Cotton F. Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology. 2006;48(3):150–9.CrossRefPubMedGoogle Scholar
  38. 38.
    Pavlisa G, Rados M, Pavlisa G, Pavic L, Potocki K, Mayer D. The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. J Clin Imaging. 2009;33(2):96–101.CrossRefGoogle Scholar
  39. 39.
    Lee EJ, terBrugge K, Mikulis D, Choi DS, Bae JM, Lee SK, et al. Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. Am J Roentgenol. 2011;196(1):71–6.CrossRefGoogle Scholar
  40. 40.
    Server A, Kulle B, Mæhlen J, Josefsen R, Schellhorn T, Kumar T, et al. Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema. Acta Radiol. 2009;50(6):682–9.CrossRefPubMedGoogle Scholar
  41. 41.
    Kallenberg K, Goldmann T, Menke J, Strik H, Bock HC, Stockhammer F, et al. Glioma infiltration of the corpus callosum: early signs detected by DTI. J Neurooncol. 2013;112(2):217–22.PubMedCentralCrossRefPubMedGoogle Scholar
  42. 42.
    Price SJ, Peña A, Burnet NG, Pickard JD, Gillard JH. Detecting glioma invasion of the corpus callosum using diffusion tensor imaging. Br J Neurosurg. 2004;18(4):391–5.CrossRefPubMedGoogle Scholar
  43. 43.
    Price SJ, Jena R, Burnet NG, Carpenter TA, Pickard JD, Gillard JH. Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur Radiol. 2007;17(7):1675–84.CrossRefPubMedGoogle Scholar
  44. 44.
    Lee EJ, Ahn KJ, Lee EK, Lee YS, Kim DB. Potential role of advanced MRI techniques for the peritumoural region in differentiating glioblastoma multiforme and solitary metastatic lesions. Clin Radiol. 2013;68:e689.CrossRefPubMedGoogle Scholar
  45. 45.
    Wang W, Steward C, Desmond P. Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. AJNR Am J Neuroradiol. 2008;30(1):203–8.CrossRefPubMedGoogle Scholar
  46. 46.
    Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Peña A, et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol. 2006;27(9):1969–74.PubMedGoogle Scholar
  47. 47.
    Partovi S, Karimi S, Lyo JK, Esmaeili A, Tan J, Deangelis LM. Multimodality imaging of primary CNS lymphoma in immunocompetent patients. Br J Radiol. 2014;87(1036):20130684.PubMedCentralCrossRefPubMedGoogle Scholar
  48. 48.
    Guo AC, Cummings TJ, Dash RC, Provenzale JM. Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology. 2002;224(1):177–83.CrossRefPubMedGoogle Scholar
  49. 49.
    Horger M, Fenchel M, Nägele T, Moehle R, Claussen CD, Beschorner R, et al. Water diffusivity: comparison of primary CNS lymphoma and astrocytic tumor infiltrating the corpus callosum. Am J Roentgenol. 2009;193(5):1384–7.CrossRefGoogle Scholar
  50. 50.
    Toh C-H, Castillo M, Wong AMC, Wei K-C, Wong H-F, Ng S-H, et al. Primary cerebral lymphoma and glioblastoma multiforme: differences in diffusion characteristics evaluated with diffusion tensor imaging. AJNR Am J Neuroradiol. 2008;29(3):471–5.CrossRefPubMedGoogle Scholar
  51. 51.
    Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007;114(2):97–109.PubMedCentralCrossRefPubMedGoogle Scholar
  52. 52.
    Tropine A, Dellani PD, Glaser M, Bohl J, Plöner T, Vucurevic G, et al. Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging. J Magn Reson Imaging. 2007;25(4):703–8.CrossRefPubMedGoogle Scholar
  53. 53.
    Toh C-H, Castillo M, Wong A-C, Wei K-C, Wong H-F, Ng S-H, et al. Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. AJNR Am J Neuroradiol. 2008;29(9):1630–5.CrossRefPubMedGoogle Scholar
  54. 54.
    Kashimura H, Inoue T, Ogasawara K, Arai H, Otawara Y, Kanbara Y, et al. Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg. 2007;107(4):784–7.CrossRefPubMedGoogle Scholar
  55. 55.
    Le Bihan D, Turner R, Douek P, Patronas N. Diffusion MR imaging: clinical applications. AJR Am J Roentgenol. 1992;159(3):591–9.CrossRefPubMedGoogle Scholar
  56. 56.
    Jolapara M, Kesavadas C, Radhakrishnan VV, Thomas B, Gupta AK, Bodhey N, et al. Role of diffusion tensor imaging in differentiating subtypes of meningiomas. J Neuroradiol. 2010;37(5):277–83.CrossRefPubMedGoogle Scholar
  57. 57.
    Horsfield MA, Jones DK. Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases - a review. NMR Biomed. 2002;15(7-8):570–7.CrossRefPubMedGoogle Scholar
  58. 58.
    Poretti A, Meoded A, Huisman TAGM. Neuroimaging of pediatric posterior fossa tumors including review of the literature. J Magn Reson Imaging. 2011;35(1):32–47.CrossRefPubMedGoogle Scholar
  59. 59.
    Rumboldt Z, Camacho DLA, Lake D, Welsh CT, Castillo M. Apparent diffusion coefficients for differentiation of cerebellar tumors in children. AJNR Am J Neuroradiol. 2006;27(6):1362–9.PubMedGoogle Scholar
  60. 60.
    Jaremko JL, Jans LBO, Coleman LT, Ditchfield MR. Value and limitations of diffusion-weighted imaging in grading and diagnosis of pediatric posterior fossa tumors. AJNR Am J Neuroradiol. 2010;31(9):1613–6.CrossRefPubMedGoogle Scholar
  61. 61.
    Kan P, Liu JK, Hedlund G, Brockmeyer DL, Walker ML, Kestle JRW. The role of diffusion-weighted magnetic resonance imaging in pediatric brain tumors. Childs Nerv Syst. 2006;22(11):1435–9.CrossRefPubMedGoogle Scholar
  62. 62.
    Poretti A, Meoded A, Cohen KJ, Grotzer MA, Boltshauser E, Huisman TAGM. Apparent diffusion coefficient of pediatric cerebellar tumors: a biomarker of tumor grade? Pediatr Blood Cancer. 2013;60:2036.PubMedGoogle Scholar
  63. 63.
    Schneider JF, Confort-Gouny S, Viola A, Le Fur Y, Viout P, Bennathan M, et al. Multiparametric differentiation of posterior fossa tumors in children using diffusion-weighted imaging and short echo-time 1H-MR spectroscopy. J Magn Reson Imaging. 2007;26(6):1390–8.CrossRefPubMedGoogle Scholar
  64. 64.
    Pillai S, Singhal A, Byrne AT, Dunham C, Cochrane DD, Steinbok P. Diffusion-weighted imaging and pathological correlation in pediatric medulloblastomas—“They are not always restricted!”. Childs Nerv Syst. 2011;27(9):1407–11.CrossRefPubMedGoogle Scholar
  65. 65.
    Bull JG, Saunders DE, Clark CA. Discrimination of paediatric brain tumours using apparent diffusion coefficient histograms. Eur Radiol. 2011;22(2):447–57.CrossRefPubMedGoogle Scholar
  66. 66.
    Gimi B, et al. Utility of apparent diffusion coefficient ratios in distinguishing common pediatric cerebellar tumors. Acad Radiol. 2020;19(7):794–800.CrossRefGoogle Scholar
  67. 67.
    Kotsenas AL, Roth TC, Manness WK, Faerber EN. Abnormal diffusion-weighted MRI in medulloblastoma: does it reflect small cell histology? Pediatr Radiol. 1999;29(7):524–6.CrossRefPubMedGoogle Scholar
  68. 68.
    Yeom KW, Mobley BC, Lober RM, Andre JB, Partap S, Vogel H, et al. Distinctive MRI features of pediatric medulloblastoma subtypes. Am J Roentgenol. 2013;200(4):895–903.CrossRefGoogle Scholar
  69. 69.
    Fruehwald-Pallamar J, Puchner SB, Rossi A, Garre ML, Cama A, Koelblinger C, et al. Magnetic resonance imaging spectrum of medulloblastoma. Neuroradiology. 2011;53(6):387–96.CrossRefPubMedGoogle Scholar
  70. 70.
    Schubert MI, Wilke M, Müller-Weihrich S, Auer DP. Diffusion-weighted magnetic resonance imaging of treatment-associated changes in recurrent and residual medulloblastoma: preliminary observations in three children. Acta Radiol. 2006;47(10):1100–4.CrossRefPubMedGoogle Scholar
  71. 71.
    Hilario A, Ramos A, Perez-Nunez A, Salvador E, Millan JM, Lagares A, et al. The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. AJNR Am J Neuroradiol. 2012;33(4):701–7.CrossRefPubMedGoogle Scholar
  72. 72.
    Maia A. MR cerebral blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. AJNR Am J Neuroradiol. 2005;26(4):777.PubMedGoogle Scholar
  73. 73.
    Scott JN, Brasher PMA, Sevick RJ, Rewcastle NB, Forsyth PA. How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology. 2002;59(6):947–9.CrossRefPubMedGoogle Scholar
  74. 74.
    Lee EJ, Lee SK, Agid R, Bae JM, Keller A, terBrugge K. Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol. 2008;29(10):1872–7.CrossRefPubMedGoogle Scholar
  75. 75.
    Kitis O, Altay H, Calli C, Yunten N, Akalin T, Yurtseven T. Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol. 2005;55(3):393–400.CrossRefPubMedGoogle Scholar
  76. 76.
    Murakami R, Hirai T, Sugahara T, Fukuoka H, Toya R, Nishimura S, et al. Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method 1. Radiology. 2009;251(3):838–45.CrossRefPubMedGoogle Scholar
  77. 77.
    Beppu T, Inoue T, Shibata Y, Kurose A, Arai H, Ogasawara K, et al. Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neurooncol. 2003;63(2):109–16.CrossRefPubMedGoogle Scholar
  78. 78.
    Stadlbauer A, Ganslandt O, Buslei R, Hammen T, Gruber S, Moser E, et al. Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology. 2006;240(3):803–10.CrossRefPubMedGoogle Scholar
  79. 79.
    Zikou AK, Alexiou GA, Kosta P, Goussia A, Astrakas L, Tsekeris P, et al. Diffusion tensor and dynamic susceptibility contrast MRI in glioblastoma. Clin Neurol Neurosurg. 2012;114(6):607–12.CrossRefPubMedGoogle Scholar
  80. 80.
    Beppu T, Inoue T, Shibata Y, Yamada N, Kurose A, Ogasawara K, et al. Fractional anisotropy value by diffusion tensor magnetic resonance imaging as a predictor of cell density and proliferation activity of glioblastomas. Surg Neurol. 2005;63(1):56–61.CrossRefPubMedGoogle Scholar
  81. 81.
    Lee HY, Na DG, Song I-C, Lee DH, Seo HS, Kim J-H, et al. Diffusion-tensor imaging for glioma grading at 3-T magnetic resonance imaging: analysis of fractional anisotropy and mean diffusivity. J Comput Assist Tomogr. 2008;32(2):298–303.CrossRefPubMedGoogle Scholar
  82. 82.
    Hui ES, Cheung MM, Qi L, Wu EX. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage. 2008;42(1):122–34.CrossRefPubMedGoogle Scholar
  83. 83.
    Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53(6):1432–40.CrossRefPubMedGoogle Scholar
  84. 84.
    Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology. 2010;254(3):876–81.CrossRefPubMedGoogle Scholar
  85. 85.
    Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology. 2012;263(2):492–501.CrossRefPubMedGoogle Scholar
  86. 86.
    Brunberg JA, Chenevert TL, McKeever PE, Ross DA, Junck LR, Muraszko KM, et al. In vivo MR determination of water diffusion coefficients and diffusion anisotropy: correlation with structural alteration in gliomas of the cerebral hemispheres. AJNR Am J Neuroradiol. 1995;16(2):361–71.PubMedGoogle Scholar
  87. 87.
    Castillo M, Smith JK, Kwock L, Wilber K. Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. AJNR Am J Neuroradiol. 2001;22(1):60–4.PubMedGoogle Scholar
  88. 88.
    De Vleeschouwer S, et al. Transient local response and persistent tumor control in a child with recurrent malignant glioma: treatment with combination therapy including dendritic cell therapy. Case report. J Neurosurg. 2004;100:492–7.Google Scholar
  89. 89.
    Sundgren PC, Fan XY, Dong Q, Weybright P, Welsh RC, Chenevert TL. Discriminating of brain tumor recurrence from radiation induced injury using diffusion tensor imaging. Proc Intl Soc Mag Reson Med. 2005;13:661.Google Scholar
  90. 90.
    Sundgren PC, Fan X, Weybright P, Welsh RC, Carlos RC, Petrou M, et al. Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging. 2006;24(9):1131–42.CrossRefPubMedGoogle Scholar
  91. 91.
    Xu J-L, Li Y-L, Lian J-M, Dou S-W, Yan F-S, Wu H, et al. Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology. 2010;52(12):1193–9.CrossRefPubMedGoogle Scholar
  92. 92.
    Weybright P. Differentiation between brain tumor recurrence and radiation injury using MR spectroscopy. Am J Roentgenol. 2005;185(6):1471–6.CrossRefGoogle Scholar
  93. 93.
    Shah R, Vattoth S, Jacob R, Manzil FFP, O’Malley JP, Borghei P, et al. Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence. Radiographics. 2012;32(5):1343–59.CrossRefPubMedGoogle Scholar
  94. 94.
    Young GS. Advanced MRI, of adult brain tumors. Neurol Clin. 2007;25(4):947–73. viii.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Frank De Belder
    • 1
  • Sophie Van Cauter
    • 2
  • Luc van den Hauwe
    • 1
  • Wim Van Hecke
    • 3
    • 1
  • Louise Emsell
    • 4
    • 5
  • Maya De Belder
    • 6
  • Matthias Spaepen
    • 1
  • Stefan Sunaert
    • 2
  • Paul M. Parizel
    • 1
  1. 1.Department of RadiologyAntwerp University HospitalAntwerpBelgium
  2. 2.Departments of Translational MRI and RadiologyKU Leuven and University Hospitals LeuvenLeuvenBelgium
  3. 3.icometrixLeuvenBelgium
  4. 4.Departments of Translational MRI and RadiologyKU Leuven and University Hospitals LeuvenLeuvenBelgium
  5. 5.Department of Old Age PsychiatryUniversitair Psychiatrisch Centrum (UPC) - KU LeuvenLeuvenBelgium
  6. 6.Department of Experimental PsychologyUniversity of GhentGhentBelgium

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